'''simple docstring'''




import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed




class        a__ (  _A					):






     def __init__(					self						:						Optional[Any]    ,							a						:						str    ,							a						:						str    ,							a						:						str=10_24    ,							a						:						Optional[int]=10_24    ,							a						:						Optional[Any]=3.6       ):





         """simple docstring"""



         __lowerCamelCase            =    tokenizer
         __lowerCamelCase            =    tokenizer.bos_token_id
         __lowerCamelCase            =    dataset
         __lowerCamelCase            =    seq_length
         __lowerCamelCase            =    seq_length * chars_per_token * num_of_sequences






     def __iter__(					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    iter(self.dataset       )
         __lowerCamelCase            =    True
         while more_examples:
             __lowerCamelCase      ,    __lowerCamelCase            =    [], 0
             while True:
                 if buffer_len >= self.input_characters:
                     break
                 try:
                     buffer.append(next(UpperCamelCase__       )['''content''']       )
                     buffer_len += len(buffer[-1]       )
                 except StopIteration:
                     __lowerCamelCase            =    False
                     break
             __lowerCamelCase            =    tokenizer(UpperCamelCase__    ,							truncation=UpperCamelCase__       )['''input_ids''']
             __lowerCamelCase            =    []
             for tokenized_input in tokenized_inputs:
                 all_token_ids.extend(tokenized_input + [self.concat_token_id]       )
             for i in range(0    ,							len(UpperCamelCase__       )    ,							self.seq_length       ):
                 __lowerCamelCase            =    all_token_ids[i : i + self.seq_length]
                 if len(UpperCamelCase__       ) == self.seq_length:
                     yield torch.tensor(UpperCamelCase__       )






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    __lowerCamelCase            =    {'''streaming''': True}
    __lowerCamelCase            =    load_dataset(args.dataset_name							,    split='''train'''							,    **A_							)
    __lowerCamelCase            =    ConstantLengthDataset(A_							,    A_							,    seq_length=args.seq_length							)
    __lowerCamelCase            =    DataLoader(A_							,    batch_size=args.batch_size							)
    return eval_dataloader






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[Any]:
    model.eval()
    __lowerCamelCase            =    []
    for step, batch in enumerate(A_							):
        with torch.no_grad():
            __lowerCamelCase            =    model(A_							,    labels=A_							)
        __lowerCamelCase            =    outputs.loss.repeat(args.batch_size							)
        losses.append(accelerator.gather(A_							)							)

        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    __lowerCamelCase            =    torch.mean(torch.cat(A_							)							)
    try:
        __lowerCamelCase            =    torch.exp(A_							)
    except OverflowError:
        __lowerCamelCase            =    float('''inf'''							)
    return loss.item(), perplexity.item()


# Setup Accelerator
__UpperCAmelCase					=Accelerator()

# Parse configuration
__UpperCAmelCase					=HfArgumentParser(EvaluationArguments)
__UpperCAmelCase					=parser.parse_args()
set_seed(args.seed)

# Logging
__UpperCAmelCase					=logging.getLogger(__name__)
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)

# Load model and tokenizer
__UpperCAmelCase					=AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__UpperCAmelCase					=AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__UpperCAmelCase					=create_dataloader(args)

# Prepare everything with our `accelerator`.
__UpperCAmelCase					=accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
__UpperCAmelCase					=evaluate(args)



logger.info(f'loss/eval: {eval_loss}, perplexity: {perplexity}')


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__lowerCAmelCase     :	List[str]       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	List[Any]       			=  OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

__lowerCAmelCase     :	Optional[Any]       			=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)

def a__       (			A_   ):






  '''simple docstring'''





  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      __magic_name__								=			model_type_to_module_name(A_   )

      __magic_name__								=			importlib.import_module(f'''.{module_name}''',	"""transformers.models"""   )
      try:
        return getattr(A_,	A_   )
      except AttributeError:
        continue

  for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
    if getattr(A_,	"""__name__""",	A_   ) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  __magic_name__								=			importlib.import_module("""transformers"""   )
  if hasattr(A_,	A_   ):
    return getattr(A_,	A_   )

  return None

def a__       (			A_,	A_ = None,	A_ = False,	A_ = False,	A_ = None,	A_ = None,	A_ = None,	A_ = False,	**A_,	):






  '''simple docstring'''





  __magic_name__								=			get_file_from_repo(
      A_,	A_,	cache_dir=A_,	force_download=A_,	resume_download=A_,	proxies=A_,	use_auth_token=A_,	revision=A_,	local_files_only=A_,	)
  if resolved_config_file is None:
    logger.info(
        """Could not locate the feature extractor configuration file, will try to use the model config instead."""   )
    return {}

  with open(A_,	encoding="""utf-8"""   ) as reader:
    return json.load(A_   )





class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Dict       )     ->      Optional[int]:







    """simple docstring"""






    raise EnvironmentError(
        """AutoFeatureExtractor is designed to be instantiated """
        """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""       )
  @classmethod
  @replace_list_option_in_docstrings(UpperCamelCase__       )
  def 				_lowercase				(					cls     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				**UpperCamelCase__     :					Optional[int]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			kwargs.pop("""config"""     ,				UpperCamelCase__       )
    __magic_name__								=			kwargs.pop("""trust_remote_code"""     ,				UpperCamelCase__       )
    __magic_name__								=			True

    __magic_name__	,       __magic_name__								=			FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    __magic_name__								=			config_dict.get("""feature_extractor_type"""     ,				UpperCamelCase__       )
    __magic_name__								=			None
    if "AutoFeatureExtractor" in config_dict.get("""auto_map"""     ,				{}       ):
      __magic_name__								=			config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

    # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
    if feature_extractor_class is None and feature_extractor_auto_map is None:
      if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			AutoConfig.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__       )
      # It could be in `config.feature_extractor_type``
      __magic_name__								=			getattr(UpperCamelCase__     ,				"""feature_extractor_type"""     ,				UpperCamelCase__       )
      if hasattr(UpperCamelCase__     ,				"""auto_map"""       ) and "AutoFeatureExtractor" in config.auto_map:
        __magic_name__								=			config.auto_map["""AutoFeatureExtractor"""]

    if feature_extractor_class is not None:
      __magic_name__								=			feature_extractor_class_from_name(UpperCamelCase__       )

    __magic_name__								=			feature_extractor_auto_map is not None
    __magic_name__								=			feature_extractor_class is not None or type(UpperCamelCase__       ) in FEATURE_EXTRACTOR_MAPPING
    __magic_name__								=			resolve_trust_remote_code(
        UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )

    if has_remote_code and trust_remote_code:
      __magic_name__								=			get_class_from_dynamic_module(
          UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__       )
      __magic_name__								=			kwargs.pop("""code_revision"""     ,				UpperCamelCase__       )
      if os.path.isdir(UpperCamelCase__       ):
        feature_extractor_class.register_for_auto_class()
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    elif feature_extractor_class is not None:
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
    elif type(UpperCamelCase__       ) in FEATURE_EXTRACTOR_MAPPING:
      __magic_name__								=			FEATURE_EXTRACTOR_MAPPING[type(UpperCamelCase__       )]
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )

    raise ValueError(
        F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
        F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
        F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()       )}'''       )





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Optional[Any]       )     ->      Dict:







    """simple docstring"""






    FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase__     ,				UpperCamelCase__       )

